论文标题
交织的蒙特卡洛树搜索和在混乱中的物体检索的自制学习
Interleaving Monte Carlo Tree Search and Self-Supervised Learning for Object Retrieval in Clutter
论文作者
论文摘要
在这项研究中，我们开发了一个机器人学习框架，首先将蒙特卡洛树搜索（MCT）应用于启用深层神经网络（DNN），以学习机器人臂和复杂场景之间的复杂相互作用，其中包含许多对象，从而使DNN部分固定MCT的行为。反过来，训练有素的DNN被整合到MCT中，以帮助指导其搜索工作。我们称这种方法学习指导的蒙特卡洛树搜索对象检索（更多），该方法可带来显着的计算效率提高并增加了解决方案最佳性。更多是一个自我监管的机器人技术框架/管道，能够在现实世界中工作，成功地体现了系统2至系统1的学习理念，而卡赫曼提出的学习理念可以很好地帮助大大加快耗时的耗时的决策过程。可以在https://github.com/arc-l/more上找到视频和补充材料
In this study, working with the task of object retrieval in clutter, we have developed a robot learning framework in which Monte Carlo Tree Search (MCTS) is first applied to enable a Deep Neural Network (DNN) to learn the intricate interactions between a robot arm and a complex scene containing many objects, allowing the DNN to partially clone the behavior of MCTS. In turn, the trained DNN is integrated into MCTS to help guide its search effort. We call this approach learning-guided Monte Carlo tree search for Object REtrieval (MORE), which delivers significant computational efficiency gains and added solution optimality. MORE is a self-supervised robotics framework/pipeline capable of working in the real world that successfully embodies the System 2 to System 1 learning philosophy proposed by Kahneman, where learned knowledge, used properly, can help greatly speed up a time-consuming decision process over time. Videos and supplementary material can be found at https://github.com/arc-l/more
